Sampling introduction: Population (N) vs Sample (n). Sampling frame: List of population units. Sampling distribution: Distribution of sample statistics. Standard error SE(x̄) = σ/√n. Central limit theorem: As n increases, x̄ approaches normal distribution regardless of population distribution. Properties: (1) Sample mean is unbiased estimator of population mean; (2) Larger n → smaller SE → more precise. Example: Population μ=100, σ=20. Sample n=25: SE = 20/5 = 4. Sample n=100: SE = 20/10 = 2. Solving: Identify population, sample, parameter. Calculate standard error. Apply CLT. Exam tip: Understand importance of sample size. Recognize sampling distribution. Practice: Inference problems.